import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt

train_X = np.linspace(-1, 1, 100)
train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.3

plotdata = {"batchsize" : [], "loss" : []}
def moving_average(a, w = 10):
    if len(a) < w:
        return a[:]
    return [val if idx < w else sum(a[(idx - w):idx]) / w for idx,val in enumerate(a)]

"""
画出原始数据散点图
plt.plot(train_X, train_Y, 'ro', label = 'Original data')
plt.legend()
plt.show()
"""

#重置图
tf.reset_default_graph()

#创建模型
#占位符
X = tf.placeholder("float")
Y = tf.placeholder("float")
#模型参数
W = tf.Variable(tf.random_normal([1]), name = "weight")
b = tf.Variable(tf.zeros([1]), name = "bias")
#前向结构
z = tf.multiply(X, W) + b
#反向优化
cost = tf.reduce_mean(tf.square(Y - z))
learning_rate = 0.01
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

#训练模型
#初始化所有变量
init = tf.global_variables_initializer()
#定义参数
training_epochs = 20
display_step = 2

#生成saver
saver = tf.train.Saver()
#生成模型的路径
savedir = "log/"

#启动session
with tf.Session() as sess:
    sess.run(init)
    #向模型中输入数据
    for epoch in range(training_epochs):
        for (x, y) in zip(train_X, train_Y):
            sess.run(optimizer, feed_dict = {X: x, Y: y})

        #显示训练中的详细信息
        if epoch % display_step == 0:
            loss = sess.run(cost, feed_dict = {X: train_X, Y: train_Y})
            print("Epoch:", epoch+1, "cost=", loss, "W=", sess.run(W), "b=", sess.run(b))
            if not(loss == "NA"):
                plotdata["batchsize"].append(epoch)
                plotdata["loss"].append(loss)

    print("Finished!")

    #保存模型
    saver.save(sess, savedir+"linermodel.cpkt")

    print("cost=", sess.run(cost, feed_dict={X: train_X, Y: train_Y}),"W=", sess.run(W),
          "b=",sess.run(b))

    #图形显示
    plt.plot(train_X, train_Y, 'ro', label = 'Original data')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label = 'Fittedline')
    plt.legend()
    plt.show()

    plotdata["avgloss"] = moving_average(plotdata["loss"])
    plt.figure(1)
    plt.subplot(211)
    plt.plot(plotdata["batchsize"], plotdata["avgloss"], 'b--')
    plt.xlabel('Minibatch number')
    plt.ylabel('Loss')
    plt.title('Minibatch run vs. Training loss')

    plt.show()

    print("x=0.2, z=", sess.run(z, feed_dict={X: 0.2}))


with tf.Session() as sess2:
    sess2.run(tf.global_variables_initializer())
    saver.restore(sess2, savedir+"linermodel.cpkt")
    print("x = 0.2, z =", sess2.run(z, feed_dict={X: .2}))


